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Hardware-Aware Color-Distorted Image Classifier for Intelligent White Balance

International Journal of Image and Graphics(2025)

Zunyi Normal Univ

Cited 0|Views3
Abstract
Automatic white balance (AWB) is an important module for cameras and classification of the color-distorted image is critical to realize intelligent AWB. Though accurate classifiers usually can be achieved via deep neural network models, they cannot fit into embedded hardware due to their complexity. To increase the classification accuracy and decrease latency, a lightweight convolutional neural network (CNN) with a histogram layer for AWB (AWBHNet) is constructed, which consists of one histogram layer, one regular convolutional layer, three depth separable convolutional layers, four pooling layers, two fully connected layers and two dropout layers. One-tenth of ImageNet is utilized as the normal image dataset. To generate various distorted colors, histogram shifting and matching are proposed to randomly adjust the histogram position or shape. Furthermore, the extent of shifting or matching is randomly generated to ensure the diversity of color distortion. Subsequently, the proposed AWBHNet and other CNNs are successively trained. Experiments show that the accuracy of the classifier trained by AWBHNet is 0.9150, which is at least 1.33% higher than each regular or lightweight network. Finally, intelligent AWB is realized on smartphones, the inference latency of AWBHNet is 47.6% lower than the best existing network.
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Key words
Automatic white balance,color-distorted image dataset,histogram layer,accuracy,latency
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要点】:论文提出了一种轻量级的卷积神经网络(AWBHNet),通过引入直方图层以提高自动白平衡中色彩失真图像分类的准确性和降低延迟。

方法】:研究构建了一个包含一个直方图层、一个常规卷积层、三个深度可分卷积层、四个池化层、两个全连接层和两个dropout层的轻量级CNN模型,并采用直方图移位和匹配方法生成色彩失真图像。

实验】:使用ImageNet数据集的十分之一作为正常图像数据集,通过直方图移位和匹配方法生成多样化的色彩失真图像,训练提出的AWBHNet及其他CNN模型。实验结果显示,AWBHNet训练的分类器准确度为0.9150,比其他常规或轻量级网络至少高出1.33%,并在智能手机上实现了智能AWB,其推理延迟比现有最佳网络低47.6%。